Zomato Project

Importing Library and reading data

import numpy as np 
import pandas as pd 
import seaborn as sns
import matplotlib.pyplot as plt

restaurants=pd.read_csv('zomato.csv',encoding = "ISO-8859-1")
countrycode=pd.read_excel('Country-Code.xlsx')

restaurants.head()

	Restaurant ID	Restaurant Name	Country Code	City	Address	Locality	Locality Verbose	Longitude	Latitude	Cuisines	...	Currency	Has Table booking	Has Online delivery	Is delivering now	Switch to order menu	Price range	Aggregate rating	Rating color	Rating text	Votes
0	6317637	Le Petit Souffle	162	Makati City	Third Floor, Century City Mall, Kalayaan Avenu...	Century City Mall, Poblacion, Makati City	Century City Mall, Poblacion, Makati City, Mak...	121.027535	14.565443	French, Japanese, Desserts	...	Botswana Pula(P)	Yes	No	No	No	3	4.8	Dark Green	Excellent	314
1	6304287	Izakaya Kikufuji	162	Makati City	Little Tokyo, 2277 Chino Roces Avenue, Legaspi...	Little Tokyo, Legaspi Village, Makati City	Little Tokyo, Legaspi Village, Makati City, Ma...	121.014101	14.553708	Japanese	...	Botswana Pula(P)	Yes	No	No	No	3	4.5	Dark Green	Excellent	591
2	6300002	Heat - Edsa Shangri-La	162	Mandaluyong City	Edsa Shangri-La, 1 Garden Way, Ortigas, Mandal...	Edsa Shangri-La, Ortigas, Mandaluyong City	Edsa Shangri-La, Ortigas, Mandaluyong City, Ma...	121.056831	14.581404	Seafood, Asian, Filipino, Indian	...	Botswana Pula(P)	Yes	No	No	No	4	4.4	Green	Very Good	270
3	6318506	Ooma	162	Mandaluyong City	Third Floor, Mega Fashion Hall, SM Megamall, O...	SM Megamall, Ortigas, Mandaluyong City	SM Megamall, Ortigas, Mandaluyong City, Mandal...	121.056475	14.585318	Japanese, Sushi	...	Botswana Pula(P)	No	No	No	No	4	4.9	Dark Green	Excellent	365
4	6314302	Sambo Kojin	162	Mandaluyong City	Third Floor, Mega Atrium, SM Megamall, Ortigas...	SM Megamall, Ortigas, Mandaluyong City	SM Megamall, Ortigas, Mandaluyong City, Mandal...	121.057508	14.584450	Japanese, Korean	...	Botswana Pula(P)	Yes	No	No	No	4	4.8	Dark Green	Excellent	229

5 rows × 21 columns

restaurants.columns

Index(['Restaurant ID', 'Restaurant Name', 'Country Code', 'City', 'Address',
       'Locality', 'Locality Verbose', 'Longitude', 'Latitude', 'Cuisines',
       'Average Cost for two', 'Currency', 'Has Table booking',
       'Has Online delivery', 'Is delivering now', 'Switch to order menu',
       'Price range', 'Aggregate rating', 'Rating color', 'Rating text',
       'Votes'],
      dtype='object')

restaurants.rename(columns={'Restaurant ID':'Restaurant_ID', 'Restaurant Name':'Restaurant_Name', 'Country Code':'Country_Code', 'Locality Verbose':'Locality_Verbose',
       'Average Cost for two':'Average_Cost_for_two', 'Has Table booking':'Has_Table_booking','Has Online delivery':'Has_Online_delivery', 'Is delivering now':'Is_delivering_now', 'Switch to order menu':'Switch_to_order_menu','Price range':'Price_range', 'Aggregate rating':'Aggregate_rating', 'Rating color':'Rating_color', 'Rating text':'Rating_text'},
                  inplace=True)

restaurants.shape

(9551, 21)

countrycode

	Country Code	Country
0	1	India
1	14	Australia
2	30	Brazil
3	37	Canada
4	94	Indonesia
5	148	New Zealand
6	162	Phillipines
7	166	Qatar
8	184	Singapore
9	189	South Africa
10	191	Sri Lanka
11	208	Turkey
12	214	UAE
13	215	United Kingdom
14	216	United States

countrycode.rename(columns={"Country Code":"Country_Code"}, inplace= True)

countrycode.columns

Index(['Country_Code', 'Country'], dtype='object')

restaurants["Average_Cost_for_two"][restaurants["Average_Cost_for_two"]==0]

84      0
85      0
87      0
201     0
240     0
277     0
346     0
397     0
407     0
634     0
637     0
639     0
677     0
851     0
2364    0
2368    0
9242    0
9254    0
Name: Average Cost for two, dtype: int64

Data Cleaning

The average cost for two column has some 0 values which is improper. Lets try removing those datas

restaurants['Average_Cost_for_two']=restaurants['Average_Cost_for_two'].replace(0,np.nan)

restaurants.isnull().sum()

Restaurant_ID            0
Restaurant_Name          0
Country_Code             0
City                     0
Address                  0
Locality                 0
Locality_Verbose         0
Longitude                0
Latitude                 0
Cuisines                 9
Average_Cost_for_two    18
Currency                 0
Has_Table_booking        0
Has_Online_delivery      0
Is_delivering_now        0
Switch_to_order_menu     0
Price_range              0
Aggregate_rating         0
Rating_color             0
Rating_text              0
Votes                    0
dtype: int64

restaurants.dropna(inplace=True)

Merging the country sheet and zomato file to get the country code

restaurants=restaurants.merge(countrycode,on='Country_Code')

restaurants.head()

	Restaurant_ID	Restaurant_Name	Country_Code	City	Address	Locality	Locality_Verbose	Longitude	Latitude	Cuisines	...	Has_Table_booking	Has_Online_delivery	Is_delivering_now	Switch_to_order_menu	Price_range	Aggregate_rating	Rating_color	Rating_text	Votes	Country
0	6317637	Le Petit Souffle	162	Makati City	Third Floor, Century City Mall, Kalayaan Avenu...	Century City Mall, Poblacion, Makati City	Century City Mall, Poblacion, Makati City, Mak...	121.027535	14.565443	French, Japanese, Desserts	...	Yes	No	No	No	3	4.8	Dark Green	Excellent	314	Phillipines
1	6304287	Izakaya Kikufuji	162	Makati City	Little Tokyo, 2277 Chino Roces Avenue, Legaspi...	Little Tokyo, Legaspi Village, Makati City	Little Tokyo, Legaspi Village, Makati City, Ma...	121.014101	14.553708	Japanese	...	Yes	No	No	No	3	4.5	Dark Green	Excellent	591	Phillipines
2	6300002	Heat - Edsa Shangri-La	162	Mandaluyong City	Edsa Shangri-La, 1 Garden Way, Ortigas, Mandal...	Edsa Shangri-La, Ortigas, Mandaluyong City	Edsa Shangri-La, Ortigas, Mandaluyong City, Ma...	121.056831	14.581404	Seafood, Asian, Filipino, Indian	...	Yes	No	No	No	4	4.4	Green	Very Good	270	Phillipines
3	6318506	Ooma	162	Mandaluyong City	Third Floor, Mega Fashion Hall, SM Megamall, O...	SM Megamall, Ortigas, Mandaluyong City	SM Megamall, Ortigas, Mandaluyong City, Mandal...	121.056475	14.585318	Japanese, Sushi	...	No	No	No	No	4	4.9	Dark Green	Excellent	365	Phillipines
4	6314302	Sambo Kojin	162	Mandaluyong City	Third Floor, Mega Atrium, SM Megamall, Ortigas...	SM Megamall, Ortigas, Mandaluyong City	SM Megamall, Ortigas, Mandaluyong City, Mandal...	121.057508	14.584450	Japanese, Korean	...	Yes	No	No	No	4	4.8	Dark Green	Excellent	229	Phillipines

5 rows × 22 columns

 restaurants.Country.value_counts()

India             8643
United States      419
United Kingdom      80
Brazil              60
South Africa        60
UAE                 60
New Zealand         40
Turkey              34
Australia           24
Phillipines         22
Indonesia           21
Qatar               20
Singapore           20
Sri Lanka           20
Canada               4
Name: Country, dtype: int64

Lets list all the country now and do analysis

indiaRes=restaurants[restaurants['Country']=='India']
singaporeRes=restaurants[restaurants['Country']=='Singapore']
AusRes=restaurants[restaurants['Country']=='Australia']
BrazRes=restaurants[restaurants['Country']=='Brazil']
CanadaRes=restaurants[restaurants['Country']=='Canada']
UAERes=restaurants[restaurants['Country']=='UAE']
UKRes=restaurants[restaurants['Country']=='United Kingdom']
USRes=restaurants[restaurants['Country']=='United States']
SouthAfricaRes=restaurants[restaurants['Country']=='South Africa']
NewZelandRes=restaurants[restaurants['Country']=='New Zealand']
TurkeyRes=restaurants[restaurants['Country']=='Turkey']
IndonesiaRes=restaurants[restaurants['Country']=='Indonesia']
QatarRes=restaurants[restaurants['Country']=='Qatar']
SrilankaRes=restaurants[restaurants['Country']=='Sri Lanka']

2) Get the Top rated restaurants in each city in India

indiaRes.head()

	Restaurant ID	Restaurant Name	Country Code	City	Address	Locality	Locality Verbose	Longitude	Latitude	Cuisines	...	Has Table booking	Has Online delivery	Is delivering now	Switch to order menu	Price range	Aggregate rating	Rating color	Rating text	Votes	Country
609	3400025	Jahanpanah	1	Agra	E 23, Shopping Arcade, Sadar Bazaar, Agra Cant...	Agra Cantt	Agra Cantt, Agra	78.011544	27.161661	North Indian, Mughlai	...	No	No	No	No	3	3.9	Yellow	Good	140	India
610	3400341	Rangrezz Restaurant	1	Agra	E-20, Shopping Arcade, Sadar Bazaar, Agra Cant...	Agra Cantt	Agra Cantt, Agra	0.000000	0.000000	North Indian, Mughlai	...	No	No	No	No	2	3.5	Yellow	Good	71	India
611	3400005	Time2Eat - Mama Chicken	1	Agra	Main Market, Sadar Bazaar, Agra Cantt, Agra	Agra Cantt	Agra Cantt, Agra	78.011608	27.160832	North Indian	...	No	No	No	No	2	3.6	Yellow	Good	94	India
612	3400021	Chokho Jeeman Marwari Jain Bhojanalya	1	Agra	1/48, Delhi Gate, Station Road, Raja Mandi, Ci...	Civil Lines	Civil Lines, Agra	77.998092	27.195928	Rajasthani	...	No	No	No	No	2	4.0	Green	Very Good	87	India
613	3400017	Pinch Of Spice	1	Agra	23/453, Opposite Sanjay Cinema, Wazipura Road,...	Civil Lines	Civil Lines, Agra	78.007553	27.201725	North Indian, Chinese, Mughlai	...	No	No	No	No	3	4.2	Green	Very Good	177	India

5 rows × 22 columns

- Get the cities in india

indiaRes.City.value_counts()

New Delhi       5473
Gurgaon         1118
Noida           1080
Faridabad        251
Ghaziabad         25
Ahmedabad         21
Guwahati          21
Lucknow           21
Bhubaneshwar      21
Amritsar          21
Goa               20
Surat             20
Ludhiana          20
Kochi             20
Vadodara          20
Aurangabad        20
Jaipur            20
Mangalore         20
Coimbatore        20
Chennai           20
Nashik            20
Bangalore         20
Pune              20
Mysore            20
Patna             20
Vizag             20
Kolkata           20
Ranchi            20
Nagpur            20
Puducherry        20
Bhopal            20
Indore            20
Mumbai            20
Allahabad         19
Dehradun          19
Chandigarh        18
Hyderabad         18
Kanpur            18
Varanasi          18
Agra              17
Secunderabad       2
Mohali             1
Panchkula          1
Name: City, dtype: int64

Top restaurant in delhi based on the rating and vote count

DelhiTopRes=indiaRes[(indiaRes['City']=='New Delhi')&(indiaRes['Aggregate_rating']>4.5)][['Aggregate_rating','Restaurant_Name','Votes']].sort_values(ascending=False,by='Votes')

DelhiTopRes

	Aggregate_rating	Restaurant_Name	Votes
2991	4.9	Naturals Ice Cream	2620
3579	4.9	Indian Accent - The Manor	1934
6637	4.6	The California Boulevard	1691
4617	4.6	Big Chill	1569
6975	4.7	Echoes Satyaniketan	1563
7827	4.6	Cafeteria & Co.	1136
3288	4.6	Spezia Bistro	1071
6635	4.7	Naturals Ice Cream	474
4276	4.9	Masala Library	408
3710	4.6	Tashan	304
7045	4.7	Pa Pa Ya	268
2992	4.7	Zabardast Indian Kitchen	242
6633	4.6	Band Baaja Baaraat	128
7158	4.6	Greenr Cafe	112
6634	4.8	Kopper Kadai	83
6404	4.6	Food Scouts	61
2990	4.7	MOB Brewpub	52

DelhiTopRes.style.apply(lambda x: ['background: green' if x.name == 'Votes' else 'background: lightsteelblue' for i in x])

	Aggregate_rating	Restaurant_Name	Votes
2991	4.900000	Naturals Ice Cream	2620
3579	4.900000	Indian Accent - The Manor	1934
6637	4.600000	The California Boulevard	1691
4617	4.600000	Big Chill	1569
6975	4.700000	Echoes Satyaniketan	1563
7827	4.600000	Cafeteria & Co.	1136
3288	4.600000	Spezia Bistro	1071
6635	4.700000	Naturals Ice Cream	474
4276	4.900000	Masala Library	408
3710	4.600000	Tashan	304
7045	4.700000	Pa Pa Ya	268
2992	4.700000	Zabardast Indian Kitchen	242
6633	4.600000	Band Baaja Baaraat	128
7158	4.600000	Greenr Cafe	112
6634	4.800000	Kopper Kadai	83
6404	4.600000	Food Scouts	61
2990	4.700000	MOB Brewpub	52

Finding out relationship between the rating and votes

sns.jointplot('Votes','Aggregate_rating',data=DelhiTopRes)

<seaborn.axisgrid.JointGrid at 0x1d83e6a8948>

There is a bit linear relationship between rating and the votes although not much, voting influences the rating

Number of Restaurants in India

India=indiaRes['Restaurant_Name'].nunique()
India

6595

Top 5 restaurants with online delivery in New Delhi

DelhiTopResOnline=indiaRes[(indiaRes['City']=='New Delhi')&(indiaRes['Aggregate_rating']>4.5) &(indiaRes['Has_Online_delivery']=='Yes')][['Aggregate_rating','Restaurant_Name','Votes','Has_Online_delivery']].sort_values(ascending=False,by='Votes')

DelhiTopResOnline.style.apply(lambda x: ['background: green' if x.name == 'Has_Online_delivery' else 'background: Royalblue' for i in x])

	Aggregate_rating	Restaurant_Name	Votes	Has_Online_delivery
2991	4.900000	Naturals Ice Cream	2620	Yes
3288	4.600000	Spezia Bistro	1071	Yes
6635	4.700000	Naturals Ice Cream	474	Yes
2992	4.700000	Zabardast Indian Kitchen	242	Yes
7158	4.600000	Greenr Cafe	112	Yes
6404	4.600000	Food Scouts	61	Yes

Cheap but best restaurants available in a city

Lets check which country is providing most online delivery option

sns.set(rc={'figure.figsize':(20,11)})
sns.countplot(restaurants['Country'],hue=restaurants['Has_Online_delivery'])

C:\Users\TOSHIBA\anaconda3\lib\site-packages\seaborn\_decorators.py:43: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.
  FutureWarning

<AxesSubplot:xlabel='Country', ylabel='count'>

Look for the cheap but best restaurants available in a New Delhi

New_Delhi=indiaRes[(indiaRes['City']=='New Delhi')&(indiaRes['Aggregate_rating']>4.5)&(indiaRes['Votes']>500)][['Average_Cost_for_two','Restaurant_Name','Aggregate_rating','Votes']].sort_values(ascending=True,by=['Average_Cost_for_two']).head()

New_Delhi

	Average_Cost_for_two	Restaurant_Name	Aggregate_rating	Votes
2991	150.0	Naturals Ice Cream	4.9	2620
6975	600.0	Echoes Satyaniketan	4.7	1563
3288	900.0	Spezia Bistro	4.6	1071
7827	900.0	Cafeteria & Co.	4.6	1136
4617	1500.0	Big Chill	4.6	1569

Top cuisines in each region.

IndianCuisines=restaurants[restaurants['Country']=='India']['Cuisines'].value_counts().head()
IndianCuisines

North Indian             936
North Indian, Chinese    511
Fast Food                348
Chinese                  340
North Indian, Mughlai    334
Name: Cuisines, dtype: int64

Aggregate rating of all the restaurants in each city in India

table=pd.pivot_table(data=indiaRes, index = ('City'), values="Aggregate_rating")

cm = sns.light_palette("green", as_cmap=True)
table.style.background_gradient(cmap=cm,axis=0)

	Aggregate_rating
City
Agra	3.917647
Ahmedabad	4.161905
Allahabad	3.400000
Amritsar	3.685714
Aurangabad	3.375000
Bangalore	4.375000
Bhopal	3.950000
Bhubaneshwar	3.980952
Chandigarh	4.050000
Chennai	4.315000
Coimbatore	4.135000
Dehradun	4.036842
Faridabad	1.866932
Ghaziabad	2.852000
Goa	4.245000
Gurgaon	2.651431
Guwahati	4.190476
Hyderabad	4.344444
Indore	3.970000
Jaipur	4.130000
Kanpur	3.822222
Kochi	4.080000
Kolkata	4.255000
Lucknow	4.195238
Ludhiana	3.980000
Mangalore	3.745000
Mohali	4.300000
Mumbai	4.085000
Mysore	3.695000
Nagpur	3.965000
Nashik	3.520000
New Delhi	2.438845
Noida	2.036204
Panchkula	4.200000
Patna	3.455000
Puducherry	3.730000
Pune	4.220000
Ranchi	3.455000
Secunderabad	4.500000
Surat	3.935000
Vadodara	4.025000
Varanasi	3.494444
Vizag	4.005000

Calculating the restaurants rating based on the rating text with percentage

indiaRes["Rating_text"].value_counts()

Average      3677
Not rated    2139
Good         1841
Very Good     691
Poor          180
Excellent     115
Name: Rating_text, dtype: int64

Does rating influences the cost of restaurant?

sns.boxplot(indiaRes["Rating_text"],indiaRes["Average_Cost_for_two"])

C:\Users\TOSHIBA\anaconda3\lib\site-packages\seaborn\_decorators.py:43: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.
  FutureWarning

<AxesSubplot:xlabel='Rating_text', ylabel='Average_Cost_for_two'>

The plot shows rating does influence the cost of a restaurant

Top Percentage cover of restaurants in a city.

plt.figure(figsize=(10,10))
indiaRes['City'].value_counts().head().plot(kind='pie',autopct='%1.1f%%')

<matplotlib.axes._subplots.AxesSubplot at 0x1fb5a0d03c8>

Top cuisines in Indian restaurants.

plt.figure(figsize=(10,10))
indiaRes['Cuisines'].value_counts().head(10).plot(kind='pie',autopct='%1.1f%%')

<AxesSubplot:ylabel='Cuisines'>

import pandas_profiling as pp

pp.ProfileReport(indiaRes)